Search Results for author: Liming Zhu

Found 17 papers, 6 papers with code

Blockchain-based Trustworthy Federated Learning Architecture

no code implementations16 Aug 2021 Sin Kit Lo, Yue Liu, Qinghua Lu, Chen Wang, Xiwei Xu, Hye-Young Paik, Liming Zhu

To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture.

Fairness Federated Learning

FLRA: A Reference Architecture for Federated Learning Systems

no code implementations22 Jun 2021 Sin Kit Lo, Qinghua Lu, Hye-Young Paik, Liming Zhu

The proposed FLRA reference architecture is based on an extensive review of existing patterns of federated learning systems found in the literature and existing industrial implementation.

Federated Learning

AI and Ethics -- Operationalising Responsible AI

no code implementations19 May 2021 Liming Zhu, Xiwei Xu, Qinghua Lu, Guido Governatori, Jon Whittle

In the last few years, AI continues demonstrating its positive impact on society while sometimes with ethically questionable consequences.

Architectural Patterns for the Design of Federated Learning Systems

no code implementations7 Jan 2021 Sin Kit Lo, Qinghua Lu, Liming Zhu, Hye-Young Paik, Xiwei Xu, Chen Wang

Therefore, in this paper, we present a collection of architectural patterns to deal with the design challenges of federated learning systems.

Federated Learning

Generating Informative CVE Description From ExploitDB Posts by Extractive Summarization

no code implementations5 Jan 2021 Jiamou Sun, Zhenchang Xing, Hao Guo, Deheng Ye, Xiaohong Li, Xiwei Xu, Liming Zhu

The extracted aspects from an ExploitDB post are then composed into a CVE description according to the suggested CVE description templates, which is must-provided information for requesting new CVEs.

Extractive Summarization Text Summarization

Meta Gradient Boosting Neural Networks

no code implementations1 Jan 2021 Manqing Dong, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu

A key challenge for meta-optimization based approaches is to determine whether an initialization condition can be generalized to tasks with diverse distributions to accelerate learning.

Meta-Learning

Generative Inverse Deep Reinforcement Learning for Online Recommendation

no code implementations4 Nov 2020 Xiaocong Chen, Lina Yao, Aixin Sun, Xianzhi Wang, Xiwei Xu, Liming Zhu

Deep reinforcement learning uses a reward function to learn user's interest and to control the learning process.

Attn-HybridNet: Improving Discriminability of Hybrid Features with Attention Fusion

2 code implementations13 Oct 2020 Sunny Verma, Chen Wang, Liming Zhu, Wei Liu

The principal component analysis network (PCANet) is an unsupervised parsimonious deep network, utilizing principal components as filters in its convolution layers.

Blockchain-based Federated Learning for Failure Detection in Industrial IoT

no code implementations6 Sep 2020 Weishan Zhang, Qinghua Lu, Qiuyu Yu, Zhaotong Li, Yue Liu, Sin Kit Lo, Shiping Chen, Xiwei Xu, Liming Zhu

Therefore, in this paper, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT.

Federated Learning

Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?

1 code implementation12 Aug 2020 Jieshan Chen, Mulong Xie, Zhenchang Xing, Chunyang Chen, Xiwei Xu, Liming Zhu, Guoqiang Li

We conduct the first large-scale empirical study of seven representative GUI element detection methods on over 50k GUI images to understand the capabilities, limitations and effective designs of these methods.

Code Generation Object Detection

A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective

no code implementations22 Jul 2020 Sin Kit Lo, Qinghua Lu, Chen Wang, Hye-Young Paik, Liming Zhu

Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates.

Federated Learning

MAMO: Memory-Augmented Meta-Optimization for Cold-start Recommendation

1 code implementation7 Jul 2020 Manqing Dong, Feng Yuan, Lina Yao, Xiwei Xu, Liming Zhu

However, most meta-learning based recommendation approaches adopt model-agnostic meta-learning for parameter initialization, where the global sharing parameter may lead the model into local optima for some users.

Meta-Learning Recommendation Systems

Survey for Trust-aware Recommender Systems: A Deep Learning Perspective

no code implementations8 Apr 2020 Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu

A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.

Recommendation Systems

Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning

1 code implementation1 Mar 2020 Jieshan Chen, Chunyang Chen, Zhenchang Xing, Xiwei Xu, Liming Zhu, Guoqiang Li, Jinshui Wang

However, the prerequisite of using screen readers is that developers have to add natural-language labels to the image-based components when they are developing the app.

Adversarial Examples on Graph Data: Deep Insights into Attack and Defense

2 code implementations5 Mar 2019 Huijun Wu, Chen Wang, Yuriy Tyshetskiy, Andrew Docherty, Kai Lu, Liming Zhu

Based on this observation, we propose a defense approach which inspects the graph and recovers the potential adversarial perturbations.

Adversarial Attack Adversarial Defense

Metric Factorization: Recommendation beyond Matrix Factorization

2 code implementations13 Feb 2018 Shuai Zhang, Lina Yao, Yi Tay, Xiwei Xu, Xiang Zhang, Liming Zhu

In the past decade, matrix factorization has been extensively researched and has become one of the most popular techniques for personalized recommendations.

Interpreting Shared Deep Learning Models via Explicable Boundary Trees

no code implementations12 Sep 2017 Huijun Wu, Chen Wang, Jie Yin, Kai Lu, Liming Zhu

In this paper, we propose a method to disclose a small set of training data that is just sufficient for users to get the insight of a complicated model.

Decision Making

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